Data Scientist, Data & AI Expert
How Did You Find Your Job?
I applied for the position on IBM’s career website and following that I had a series
of interviews before I got an offer.
At IBM, Data scientists work with enterprise leaders and key decision makers to solve
business problems by preparing, analyzing, and understanding data to deliver insight,
predict emerging trends, and provide recommendations to optimize results. Data scientists
use a variety of data (structured, unstructured, IoT streaming), analytics, AI tools,
and programming languages often using a cloud infrastructure to handle the volume
and veracity of data streams. Modern applications of data science range from traditional
transactional data analytics to natural language processing and computer vision, with
a variety of analytical tools, machine learning and AI algorithms. Armed with data,
modeling expertise, and analytic results, the data scientist communicates conclusions
and recommendations to stakeholders in an organization's leadership structure. Business
acumen is an important skill for data scientists, for example, in understanding the
problem, formulating hypotheses and testing conclusions to determine appropriate methods
to influence strategic choices through data. To effectively communicate their findings
to business leaders, data scientists need strong consulting, communication, visualization,
and storytelling skills.
Structure of a Typical Day
I work in the Cloud garage at IBM which has a start-up culture of building fast and
we are very agile in the way we work. I do not have a typical day as I travel frequently
to client offices in different time zones. I help clients on a 6-8-week basis as an
expert to understand their pain points with regards to discovering efficient use of
data using machine learning techniques (sometimes out of the box Watson products)
by building a prototype/MVP (minimum viable product) for them which could solve their
business problems. We work in a collaborative way to engage the corresponding data
scientists from the client’s organization so that once we develop the prototype, they
can carry our methodology forward and continue building on the MVP delivered to achieve
Advice to Other Students
There is no substitute to hard work. Be passionate about what you do and always plan
for the long run.